- Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
- Rather than train a single neural network to recognize pictures, researchers train two competing networks.
- “The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
- Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
- Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.
Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
Continue reading “Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center”